In this report we explore the possibility of using cGAN (Conditional Generative Adversarial Networks) for performing automatic graphic operations on the photographs or videos of human faces, similar to those typically done manually using a software tool such as Photoshop or After Effects, by learning from examples. Motivation A good part of my research in Machine Learning has to »
Is it possible to train a cGAN (Conditional Generative Adversarial Networks) model for monocular depth perception? If the answer is yes, then it would mean that we have a way to allow an artificial system to acquire some basic concept about distance in the physical world, learning from only flat images, starting with nothing. The type of training proposed »
In this report we study the possibility of building the neural model of human faces using cGAN. In my last experiment Generate Photo-realistic Avatars with DCGAN I showed that it is possible to use DCGAN (Deep Convolutional Generative Adversarial Networks), the non-conditional variation of GAN, to synthesize photo-realistic animated facial expressions using a model trained from limited number of »
In this report we explore the feasibility of using DCGAN (Deep Convolutional Generative Adversarial Networks) to generate the neural model of a specific person from limited amount of images or videos, with the aim of creating a controllable avatar with photo-realistic animated expressions out of such a neural model. Here DCGAN holds the promise that the neural model created »